Personalized generation usually involves the IDs of users and items.
Previous approaches typically adopt multi-layer perceptron (MLP) to encode the IDs into a context vector, from which RNN can decode a word sequence.
This strategy can be found in many applications, such as review generation [1]}, tip generation [2]} and explanation generation [3]}.
However, it does not fit Transformer that relies entirely on self-attention.
Probably because a proper solution to deal with heterogeneous inputs (i.e., IDs and words) is yet to be found, previous works with Transformer for personalized generation replace IDs with text segments, such as persona attributes [4]}, movie titles [5]} and item features [6]}, which are in the same semantic space as the word sequence to be generated.
In comparison, our solution is to design an effective task that can give the IDs linguistic meanings, thus connecting IDs with words.
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